Difference between revisions of "AQ 2007 10 31 Discussion"
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− | [[ | + | [[Workshop|''back to 2007-10-31 Workshop page'']] |
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==Data/tool Decision Tree== | ==Data/tool Decision Tree== | ||
− | + | ::wonder if we could set up table comparing products | |
− | + | ::might do by datasource, visualization tool, processing tool, etc. | |
==Usage considerations== | ==Usage considerations== | ||
===Legal=== | ===Legal=== | ||
*EPA always has to defend its judgments in court | *EPA always has to defend its judgments in court | ||
*Court needs "preponderance of evidence" or "beyond a reasonable doubt" | *Court needs "preponderance of evidence" or "beyond a reasonable doubt" | ||
− | + | ::This is quite different from 0.95% certainty. | |
===Science=== | ===Science=== | ||
*Needs detailed information about sources and models used in "correcting" data | *Needs detailed information about sources and models used in "correcting" data |
Revision as of 12:04, October 31, 2007
back to 2007-10-31 Workshop page
Data/tool Decision Tree
- wonder if we could set up table comparing products
- might do by datasource, visualization tool, processing tool, etc.
Usage considerations
Legal
- EPA always has to defend its judgments in court
- Court needs "preponderance of evidence" or "beyond a reasonable doubt"
- This is quite different from 0.95% certainty.
Science
- Needs detailed information about sources and models used in "correcting" data
Education
- Special considerations for "real-time" data
- Special considerations for hiding and introducing complexity
Data lineage tracking
- Do we need to coordinate use of conventions, even in readme files, for tracking observations?
- How do we track sources and magnitude of variance within and across datasets throughout the processing chain?
- Take as an example the case x and y variance are not the same.
- Can we list sources of variance that must be taken into consideration as we visualize or composite datasets?
- Cloud cover
- Registration issues
- Instrument issues
- resolution
- interference (NOx)
Data quality considerations
- What is the best way to determine "best available evidence"?
- How do we know if a remote sensing product has been verified with ground data and in situations comparable to use?